I'm an AI engineer and researcher with a focus on Machine Learning, Deep Learning, Computer Vision, and Large Language Model systems. I design, train, optimize, and deploy scalable AI architectures that improve model accuracy, reduce inference latency, and boost production performance. I thrive at the intersection of research and production, delivering RAG-based retrieval, memory-efficient conversational AI, and cloud deployment solutions in healthcare, surveillance, and enterprise automation. I have hands-on experience leading AI lifecycles, building ETL pipelines, and driving end-to-end deployments on GCP, Firebase, and CI/CD pipelines, while continually refining models through experimentation and robust evaluation workflows.

Shreyash Wetal

I'm an AI engineer and researcher with a focus on Machine Learning, Deep Learning, Computer Vision, and Large Language Model systems. I design, train, optimize, and deploy scalable AI architectures that improve model accuracy, reduce inference latency, and boost production performance. I thrive at the intersection of research and production, delivering RAG-based retrieval, memory-efficient conversational AI, and cloud deployment solutions in healthcare, surveillance, and enterprise automation. I have hands-on experience leading AI lifecycles, building ETL pipelines, and driving end-to-end deployments on GCP, Firebase, and CI/CD pipelines, while continually refining models through experimentation and robust evaluation workflows.

Available to hire

I’m an AI engineer and researcher with a focus on Machine Learning, Deep Learning, Computer Vision, and Large Language Model systems. I design, train, optimize, and deploy scalable AI architectures that improve model accuracy, reduce inference latency, and boost production performance. I thrive at the intersection of research and production, delivering RAG-based retrieval, memory-efficient conversational AI, and cloud deployment solutions in healthcare, surveillance, and enterprise automation.

I have hands-on experience leading AI lifecycles, building ETL pipelines, and driving end-to-end deployments on GCP, Firebase, and CI/CD pipelines, while continually refining models through experimentation and robust evaluation workflows.

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Language

English
Fluent

Work Experience

Artificial Intelligence Engineer (Team Lead) at The Modern Group
June 1, 2025 - December 31, 2025
Architected and deployed LLM-based systems using RAG pipelines and vector retrieval, focusing on long-term contextual information retrieval and memory-efficient conversational AI systems, improving retrieval accuracy by 30%. Designed prompt engineering and LLM decision pipelines for context-aware reasoning and data-driven decision support systems, increasing contextual relevance by 25%. Built scalable real-time inference APIs integrated with Firebase and GCP, reducing deployment cycle time by 40%. Designed ETL pipelines and dimensional data models supporting analytics for 100K+ records. Reduced inference latency and cloud costs by 20% through model optimization and batching strategies. Led AI lifecycle management including experimentation, CI/CD integration, deployment, and monitoring.
Research Student (Artificial Intelligence) at University of Sydney
March 1, 2025 - Present
Designed and trained deep CNN architectures for medical image semantic segmentation, improving Dice score by 12%. Implemented preprocessing, augmentation, and cross-validation pipelines increasing model generalization across datasets. Performed hyperparameter tuning using grid and Bayesian optimization, reducing validation loss by 18%. Benchmarked U-Net and DeepLabV3+ architectures, optimizing inference efficiency and robustness. Automated evaluation workflows using IoU, F1-score, precision-recall, and ROC metrics for reproducible experimentation.
Associate Prompt Engineer at NVIDIA
October 1, 2024 - December 31, 2024
Engineered enterprise-grade prompt frameworks improving LLM response precision by 22%. Conducted structured A/B testing across multiple transformer architectures to enhance output consistency. Applied reinforcement learning feedback strategies to improve generation reliability. Developed automated prompt evaluation pipelines to standardize NLP benchmarking.

Education

Master of Computer Science – Artificial Intelligence and Data Science at University of Sydney
January 1, 2025 - January 1, 2027
Bachelor of Technology in Information Technology at Pimpri Chinchwad College of Engineering
January 1, 2020 - January 1, 2024
Master of Computer Science – Artificial Intelligence and Data Science at University of Sydney
January 1, 2025 - December 31, 2027
Bachelor of Technology in Information Technology at Pimpri Chinchwad College of Engineering
January 1, 2020 - December 31, 2024

Qualifications

DeepLearning.AI TensorFlow Developer Professional Certificate
January 11, 2030 - April 13, 2026
Deep Learning Specialization
January 11, 2030 - April 13, 2026
LangChain LLM Applications
January 11, 2030 - April 13, 2026
JP Morgan Quantitative Modelling (Forage)
January 11, 2030 - April 13, 2026
DeepLearning.AI TensorFlow Developer Professional Certificate
January 11, 2030 - June 27, 2026
Deep Learning Specialization
January 11, 2030 - June 27, 2026
LangChain LLM Applications
January 11, 2030 - June 27, 2026
JP Morgan Quantitative Modelling (Forage)
January 11, 2030 - June 27, 2026

Industry Experience

Healthcare, Software & Internet, Professional Services, Education